#
EasyAnimateTransformer3DModel

A Diffusion Transformer model for 3D data from EasyAnimate was introduced by Alibaba PAI.

The model can be loaded with the following code snippet.

    from diffusers import EasyAnimateTransformer3DModel

    transformer = EasyAnimateTransformer3DModel.from_pretrained("alibaba-pai/EasyAnimateV5.1-12b-zh", subfolder="transformer", torch_dtype=torch.float16).to("cuda")

##
EasyAnimateTransformer3DModel

###
class diffusers.EasyAnimateTransformer3DModel

< source >

( num_attention_heads: int = 48 attention_head_dim: int = 64 in_channels: typing.Optional[int] = None out_channels: typing.Optional[int] = None patch_size: typing.Optional[int] = None sample_width: int = 90 sample_height: int = 60 activation_fn: str = 'gelu-approximate' timestep_activation_fn: str = 'silu' freq_shift: int = 0 num_layers: int = 48 mmdit_layers: int = 48 dropout: float = 0.0 time_embed_dim: int = 512 add_norm_text_encoder: bool = False text_embed_dim: int = 3584 text_embed_dim_t5: int = None norm_eps: float = 1e-05 norm_elementwise_affine: bool = True flip_sin_to_cos: bool = True time_position_encoding_type: str = '3d_rope' after_norm = False resize_inpaint_mask_directly: bool = True enable_text_attention_mask: bool = True add_noise_in_inpaint_model: bool = True )

Parameters

- num_attention_heads (`int`, defaults to `48`) — The number of heads to use for multi-head attention.
- attention_head_dim (`int`, defaults to `64`) — The number of channels in each head.
- in_channels (`int`, defaults to `16`) — The number of channels in the input.
- out_channels (`int`, optional, defaults to `16`) — The number of channels in the output.
- patch_size (`int`, defaults to `2`) — The size of the patches to use in the patch embedding layer.
- sample_width (`int`, defaults to `90`) — The width of the input latents.
- sample_height (`int`, defaults to `60`) — The height of the input latents.
- activation_fn (`str`, defaults to `"gelu-approximate"`) — Activation function to use in feed-forward.
- timestep_activation_fn (`str`, defaults to `"silu"`) — Activation function to use when generating the timestep embeddings.
- num_layers (`int`, defaults to `30`) — The number of layers of Transformer blocks to use.
- mmdit_layers (`int`, defaults to `1000`) — The number of layers of Multi Modal Transformer blocks to use.
- dropout (`float`, defaults to `0.0`) — The dropout probability to use.
- time_embed_dim (`int`, defaults to `512`) — Output dimension of timestep embeddings.
- text_embed_dim (`int`, defaults to `4096`) — Input dimension of text embeddings from the text encoder.
- norm_eps (`float`, defaults to `1e-5`) — The epsilon value to use in normalization layers.
- norm_elementwise_affine (`bool`, defaults to `True`) — Whether to use elementwise affine in normalization layers.
- flip_sin_to_cos (`bool`, defaults to `True`) — Whether to flip the sin to cos in the time embedding.
- time_position_encoding_type (`str`, defaults to `3d_rope`) — Type of time position encoding.
- after_norm (`bool`, defaults to `False`) — Flag to apply normalization after.
- resize_inpaint_mask_directly (`bool`, defaults to `True`) — Flag to resize inpaint mask directly.
- enable_text_attention_mask (`bool`, defaults to `True`) — Flag to enable text attention mask.
- add_noise_in_inpaint_model (`bool`, defaults to `False`) — Flag to add noise in inpaint model.

A Transformer model for video-like data in EasyAnimate.

##
Transformer2DModelOutput

###
class diffusers.models.modeling_outputs.Transformer2DModelOutput

< source >

( sample: torch.Tensor )

Parameters

- sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if Transformer2DModel is discrete) — The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability distributions for the unnoised latent pixels.

The output of Transformer2DModel.

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